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As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a most intense hurricane. While I am unprepared to predict that strength yet due to path variability, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the system drifts over very warm sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the first to outperform standard meteorological experts at their specialty. Across all tropical systems so far this year, the AI is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way The System Functions

Google’s model operates through spotting patterns that conventional lengthy physics-based prediction systems may overlook.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” he said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been used in research fields like meteorology for years – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to generate an result, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have used for decades that can require many hours to run and require the largest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the fact that the AI could outperform earlier top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin said that while Google DeepMind is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he plans to talk with Google about how it can enhance the AI results even more helpful for experts by providing additional internal information they can use to evaluate the reasons it is producing its conclusions.

“A key concern that nags at me is that although these forecasts appear really, really good, the results of the model is essentially a black box,” remarked Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has developed a high-performance forecasting system which allows researchers a peek into its methods – unlike most systems which are provided free to the general audience in their entirety by the governments that designed and maintain them.

Google is not the only one in adopting artificial intelligence to address challenging weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.